public interface ConfigProtoOrBuilder
extends org.nd4j.shade.protobuf.MessageOrBuilder
| Modifier and Type | Method and Description |
|---|---|
boolean |
containsDeviceCount(String key)
Map from device type name (e.g., "CPU" or "GPU" ) to maximum
number of devices of that type to use.
|
boolean |
getAllowSoftPlacement()
Whether soft placement is allowed.
|
ClusterDef |
getClusterDef()
Optional list of all workers to use in this session.
|
ClusterDefOrBuilder |
getClusterDefOrBuilder()
Optional list of all workers to use in this session.
|
Map<String,Integer> |
getDeviceCount()
Deprecated.
|
int |
getDeviceCountCount()
Map from device type name (e.g., "CPU" or "GPU" ) to maximum
number of devices of that type to use.
|
Map<String,Integer> |
getDeviceCountMap()
Map from device type name (e.g., "CPU" or "GPU" ) to maximum
number of devices of that type to use.
|
int |
getDeviceCountOrDefault(String key,
int defaultValue)
Map from device type name (e.g., "CPU" or "GPU" ) to maximum
number of devices of that type to use.
|
int |
getDeviceCountOrThrow(String key)
Map from device type name (e.g., "CPU" or "GPU" ) to maximum
number of devices of that type to use.
|
String |
getDeviceFilters(int index)
When any filters are present sessions will ignore all devices which do not
match the filters.
|
org.nd4j.shade.protobuf.ByteString |
getDeviceFiltersBytes(int index)
When any filters are present sessions will ignore all devices which do not
match the filters.
|
int |
getDeviceFiltersCount()
When any filters are present sessions will ignore all devices which do not
match the filters.
|
List<String> |
getDeviceFiltersList()
When any filters are present sessions will ignore all devices which do not
match the filters.
|
ConfigProto.Experimental |
getExperimental()
.tensorflow.ConfigProto.Experimental experimental = 16; |
ConfigProto.ExperimentalOrBuilder |
getExperimentalOrBuilder()
.tensorflow.ConfigProto.Experimental experimental = 16; |
GPUOptions |
getGpuOptions()
Options that apply to all GPUs.
|
GPUOptionsOrBuilder |
getGpuOptionsOrBuilder()
Options that apply to all GPUs.
|
GraphOptions |
getGraphOptions()
Options that apply to all graphs.
|
GraphOptionsOrBuilder |
getGraphOptionsOrBuilder()
Options that apply to all graphs.
|
int |
getInterOpParallelismThreads()
Nodes that perform blocking operations are enqueued on a pool of
inter_op_parallelism_threads available in each process.
0 means the system picks an appropriate number.
|
int |
getIntraOpParallelismThreads()
The execution of an individual op (for some op types) can be
parallelized on a pool of intra_op_parallelism_threads.
0 means the system picks an appropriate number.
|
boolean |
getIsolateSessionState()
If true, any resources such as Variables used in the session will not be
shared with other sessions.
|
boolean |
getLogDevicePlacement()
Whether device placements should be logged.
|
long |
getOperationTimeoutInMs()
Global timeout for all blocking operations in this session.
|
int |
getPlacementPeriod()
Assignment of Nodes to Devices is recomputed every placement_period
steps until the system warms up (at which point the recomputation
typically slows down automatically).
|
RPCOptions |
getRpcOptions()
Options that apply when this session uses the distributed runtime.
|
RPCOptionsOrBuilder |
getRpcOptionsOrBuilder()
Options that apply when this session uses the distributed runtime.
|
ThreadPoolOptionProto |
getSessionInterOpThreadPool(int index)
This option is experimental - it may be replaced with a different mechanism
in the future.
|
int |
getSessionInterOpThreadPoolCount()
This option is experimental - it may be replaced with a different mechanism
in the future.
|
List<ThreadPoolOptionProto> |
getSessionInterOpThreadPoolList()
This option is experimental - it may be replaced with a different mechanism
in the future.
|
ThreadPoolOptionProtoOrBuilder |
getSessionInterOpThreadPoolOrBuilder(int index)
This option is experimental - it may be replaced with a different mechanism
in the future.
|
List<? extends ThreadPoolOptionProtoOrBuilder> |
getSessionInterOpThreadPoolOrBuilderList()
This option is experimental - it may be replaced with a different mechanism
in the future.
|
boolean |
getUsePerSessionThreads()
If true, use a new set of threads for this session rather than the global
pool of threads.
|
boolean |
hasClusterDef()
Optional list of all workers to use in this session.
|
boolean |
hasExperimental()
.tensorflow.ConfigProto.Experimental experimental = 16; |
boolean |
hasGpuOptions()
Options that apply to all GPUs.
|
boolean |
hasGraphOptions()
Options that apply to all graphs.
|
boolean |
hasRpcOptions()
Options that apply when this session uses the distributed runtime.
|
findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof
int getDeviceCountCount()
Map from device type name (e.g., "CPU" or "GPU" ) to maximum number of devices of that type to use. If a particular device type is not found in the map, the system picks an appropriate number.
map<string, int32> device_count = 1;
boolean containsDeviceCount(String key)
Map from device type name (e.g., "CPU" or "GPU" ) to maximum number of devices of that type to use. If a particular device type is not found in the map, the system picks an appropriate number.
map<string, int32> device_count = 1;
@Deprecated Map<String,Integer> getDeviceCount()
getDeviceCountMap()
instead.Map<String,Integer> getDeviceCountMap()
Map from device type name (e.g., "CPU" or "GPU" ) to maximum number of devices of that type to use. If a particular device type is not found in the map, the system picks an appropriate number.
map<string, int32> device_count = 1;
int getDeviceCountOrDefault(String key, int defaultValue)
Map from device type name (e.g., "CPU" or "GPU" ) to maximum number of devices of that type to use. If a particular device type is not found in the map, the system picks an appropriate number.
map<string, int32> device_count = 1;
int getDeviceCountOrThrow(String key)
Map from device type name (e.g., "CPU" or "GPU" ) to maximum number of devices of that type to use. If a particular device type is not found in the map, the system picks an appropriate number.
map<string, int32> device_count = 1;
int getIntraOpParallelismThreads()
The execution of an individual op (for some op types) can be parallelized on a pool of intra_op_parallelism_threads. 0 means the system picks an appropriate number.
int32 intra_op_parallelism_threads = 2;
int getInterOpParallelismThreads()
Nodes that perform blocking operations are enqueued on a pool of inter_op_parallelism_threads available in each process. 0 means the system picks an appropriate number. Note that the first Session created in the process sets the number of threads for all future sessions unless use_per_session_threads is true or session_inter_op_thread_pool is configured.
int32 inter_op_parallelism_threads = 5;
boolean getUsePerSessionThreads()
If true, use a new set of threads for this session rather than the global pool of threads. Only supported by direct sessions. If false, use the global threads created by the first session, or the per-session thread pools configured by session_inter_op_thread_pool. This option is deprecated. The same effect can be achieved by setting session_inter_op_thread_pool to have one element, whose num_threads equals inter_op_parallelism_threads.
bool use_per_session_threads = 9;
List<ThreadPoolOptionProto> getSessionInterOpThreadPoolList()
This option is experimental - it may be replaced with a different mechanism in the future. Configures session thread pools. If this is configured, then RunOptions for a Run call can select the thread pool to use. The intended use is for when some session invocations need to run in a background pool limited to a small number of threads: - For example, a session may be configured to have one large pool (for regular compute) and one small pool (for periodic, low priority work); using the small pool is currently the mechanism for limiting the inter-op parallelism of the low priority work. Note that it does not limit the parallelism of work spawned by a single op kernel implementation. - Using this setting is normally not needed in training, but may help some serving use cases. - It is also generally recommended to set the global_name field of this proto, to avoid creating multiple large pools. It is typically better to run the non-low-priority work, even across sessions, in a single large pool.
repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12;
ThreadPoolOptionProto getSessionInterOpThreadPool(int index)
This option is experimental - it may be replaced with a different mechanism in the future. Configures session thread pools. If this is configured, then RunOptions for a Run call can select the thread pool to use. The intended use is for when some session invocations need to run in a background pool limited to a small number of threads: - For example, a session may be configured to have one large pool (for regular compute) and one small pool (for periodic, low priority work); using the small pool is currently the mechanism for limiting the inter-op parallelism of the low priority work. Note that it does not limit the parallelism of work spawned by a single op kernel implementation. - Using this setting is normally not needed in training, but may help some serving use cases. - It is also generally recommended to set the global_name field of this proto, to avoid creating multiple large pools. It is typically better to run the non-low-priority work, even across sessions, in a single large pool.
repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12;
int getSessionInterOpThreadPoolCount()
This option is experimental - it may be replaced with a different mechanism in the future. Configures session thread pools. If this is configured, then RunOptions for a Run call can select the thread pool to use. The intended use is for when some session invocations need to run in a background pool limited to a small number of threads: - For example, a session may be configured to have one large pool (for regular compute) and one small pool (for periodic, low priority work); using the small pool is currently the mechanism for limiting the inter-op parallelism of the low priority work. Note that it does not limit the parallelism of work spawned by a single op kernel implementation. - Using this setting is normally not needed in training, but may help some serving use cases. - It is also generally recommended to set the global_name field of this proto, to avoid creating multiple large pools. It is typically better to run the non-low-priority work, even across sessions, in a single large pool.
repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12;
List<? extends ThreadPoolOptionProtoOrBuilder> getSessionInterOpThreadPoolOrBuilderList()
This option is experimental - it may be replaced with a different mechanism in the future. Configures session thread pools. If this is configured, then RunOptions for a Run call can select the thread pool to use. The intended use is for when some session invocations need to run in a background pool limited to a small number of threads: - For example, a session may be configured to have one large pool (for regular compute) and one small pool (for periodic, low priority work); using the small pool is currently the mechanism for limiting the inter-op parallelism of the low priority work. Note that it does not limit the parallelism of work spawned by a single op kernel implementation. - Using this setting is normally not needed in training, but may help some serving use cases. - It is also generally recommended to set the global_name field of this proto, to avoid creating multiple large pools. It is typically better to run the non-low-priority work, even across sessions, in a single large pool.
repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12;
ThreadPoolOptionProtoOrBuilder getSessionInterOpThreadPoolOrBuilder(int index)
This option is experimental - it may be replaced with a different mechanism in the future. Configures session thread pools. If this is configured, then RunOptions for a Run call can select the thread pool to use. The intended use is for when some session invocations need to run in a background pool limited to a small number of threads: - For example, a session may be configured to have one large pool (for regular compute) and one small pool (for periodic, low priority work); using the small pool is currently the mechanism for limiting the inter-op parallelism of the low priority work. Note that it does not limit the parallelism of work spawned by a single op kernel implementation. - Using this setting is normally not needed in training, but may help some serving use cases. - It is also generally recommended to set the global_name field of this proto, to avoid creating multiple large pools. It is typically better to run the non-low-priority work, even across sessions, in a single large pool.
repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12;
int getPlacementPeriod()
Assignment of Nodes to Devices is recomputed every placement_period steps until the system warms up (at which point the recomputation typically slows down automatically).
int32 placement_period = 3;
List<String> getDeviceFiltersList()
When any filters are present sessions will ignore all devices which do not match the filters. Each filter can be partially specified, e.g. "/job:ps" "/job:worker/replica:3", etc.
repeated string device_filters = 4;
int getDeviceFiltersCount()
When any filters are present sessions will ignore all devices which do not match the filters. Each filter can be partially specified, e.g. "/job:ps" "/job:worker/replica:3", etc.
repeated string device_filters = 4;
String getDeviceFilters(int index)
When any filters are present sessions will ignore all devices which do not match the filters. Each filter can be partially specified, e.g. "/job:ps" "/job:worker/replica:3", etc.
repeated string device_filters = 4;
org.nd4j.shade.protobuf.ByteString getDeviceFiltersBytes(int index)
When any filters are present sessions will ignore all devices which do not match the filters.  If allow_soft_placement is true, an op will be placed on CPU if 1. there's no GPU implementation for the OP or 2. no GPU devices are known or registered or 3. need to co-locate with reftype input(s) which are from CPU.
bool allow_soft_placement = 7;
boolean getLogDevicePlacement()
Whether device placements should be logged.
bool log_device_placement = 8;
boolean hasGraphOptions()
Options that apply to all graphs.
.tensorflow.GraphOptions graph_options = 10;
GraphOptions getGraphOptions()
Options that apply to all graphs.
.tensorflow.GraphOptions graph_options = 10;
GraphOptionsOrBuilder getGraphOptionsOrBuilder()
Options that apply to all graphs.
.tensorflow.GraphOptions graph_options = 10;
long getOperationTimeoutInMs()
Global timeout for all blocking operations in this session. If non-zero, and not overridden on a per-operation basis, this value will be used as the deadline for all blocking operations.
int64 operation_timeout_in_ms = 11;
boolean hasRpcOptions()
Options that apply when this session uses the distributed runtime.
.tensorflow.RPCOptions rpc_options = 13;
RPCOptions getRpcOptions()
Options that apply when this session uses the distributed runtime.
.tensorflow.RPCOptions rpc_options = 13;
RPCOptionsOrBuilder getRpcOptionsOrBuilder()
Options that apply when this session uses the distributed runtime.
.tensorflow.RPCOptions rpc_options = 13;
boolean hasClusterDef()
Optional list of all workers to use in this session.
.tensorflow.ClusterDef cluster_def = 14;
ClusterDef getClusterDef()
Optional list of all workers to use in this session.
.tensorflow.ClusterDef cluster_def = 14;
ClusterDefOrBuilder getClusterDefOrBuilder()
Optional list of all workers to use in this session.
.tensorflow.ClusterDef cluster_def = 14;
boolean getIsolateSessionState()
If true, any resources such as Variables used in the session will not be shared with other sessions.
bool isolate_session_state = 15;
boolean hasExperimental()
.tensorflow.ConfigProto.Experimental experimental = 16;
ConfigProto.Experimental getExperimental()
.tensorflow.ConfigProto.Experimental experimental = 16;
ConfigProto.ExperimentalOrBuilder getExperimentalOrBuilder()
.tensorflow.ConfigProto.Experimental experimental = 16;
Copyright © 2020. All rights reserved.


| Class | Description |
|---|---|
| AbstractMonteCarloPlayer |
Holds common code shared by all monte-carlo players
|
| AStarPlayer |
A* algorithm player that utilizes a heuristic
|
| BreadthFirstSearchPlayer |
Standard Breadth-first-search player for one-player games.
|
| CameraPlayer |
Camera player is utilizing the camera controller object to read the
moves made by human on the real-world board
|
| DepthFirstSearchPlayer |
Standard Depth-first-search player for one-player games.
|
| MiniMaxPlayer |
Minimax player searches the game tree in a breadth-first search manner,
taking the best result on the players move and the worst result on the
opponent move into account.
|
| MiniMaxStochasticPlayer |
Extension of the standard minimax player that considers only some of
the moves in each game state hoping to search more in depth
|
| MonteCarloClassicPlayer |
This is the default version of monte carlo player, it only
looks at the number of won trials compared to the number of all trials
|
| MonteCarloRatioPlayer |
A version of a monte-carlo player that calculates the winning trials
scaled with a quotient that is calculated as a multiply of 1/N_i constants,
where N_i is branching in the opponent move state.
|
| MonteCarloRatioPlayer2 |
Monte carlo player where trials have differnt weight - depending
on the depth of the trial - the deeper the trial (the more moves needed),
the lower its contribution to the overall scores
|
| MousePlayer |
Mouse player represents a human playing using/clicking the mouse in the
window with graphical visualisation of the game board
|
| Player |
A general super-class for all player types.
|
| RandomGeneralPlayer |
Always plays a random move - can be used in any game
|
| Enum | Description |
|---|---|
| MiniMaxPlayer.NodeType |
opened nodes are MAX if it is this player's turn, states when it is opponent's turn are MIN
|
| Player.boardType |
the type of game board where the game can be played
|
| Player.playerType |
the types of players
|